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FAST CLUSTERING ALGORITHM BASED ON KERNEL FUZZY C-MEANS INTEGRATED WITH SPATIAL CONSTRAINTS
FAST CLUSTERING ALGORITHM BASED ON KERNEL FUZZY C-MEANS INTEGRATED WITH SPATIAL CONSTRAINTS
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机译：基于内核模糊C型算法的快速聚类算法与空间约束集成
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摘要
A fast clustering algorithm of kernel fuzzy C-means integrated with spatial constraints, including (1) applying the illumination processing algorithm, the preprocessed image affected by illumination is constructed; (2) After step (1), the original image and preprocessed image are mapped to the feature space using Gaussian kernel to cluster and segment. Providing a defect segmentation method for fluorescent glue which is robust to illumination to process and calculate the illuminated image, so as to complete the detection of foreign matters, bubbles and discoloration defects of fluorescent glue in lighting products. The disclosure provides a fast clustering algorithm of kernel fuzzy C-means integrated with spatial constraints. The image is mapped into the feature space, and the objective function of kernel fuzzy C-means clustering is optimized by using the spatial relationship of pixels, so that the clustering process has segmentation robustness to the gray value change of similar pixels caused by environmental changes.
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